PeerGPT：探究基于 LLM 的同伴智能体作为团队协调者与参与者，在儿童协作学习中的角色
PeerGPT: Probing the Roles of LLM-based Peer Agents as Team Moderators and Participants in Children's Collaborative Learning
AI总结：
本研究探究基于LLM的同伴智能体在儿童协作学习中作为团队协调者与参与者的角色，发现其能有效管理讨论并激发创造力，但存在指令被无视和反馈不及时的问题，为未来设计改进提供了依据。
摘要：
在儿童协作学习中，有效的同伴对话能显著提升协作互动的质量。将大型语言模型（LLM）智能体引入该场景，探索其作为同伴的新角色，评估其作为团队协调者与参与者的影响。我们邀请两组参与者参加协作学习工作坊，让他们讨论并提出设计问题的概念解决方案。使用主题分析法对同伴对话记录进行分析。我们发现，同伴智能体作为团队协调者能有效管理讨论，但有时其指令会被无视。作为参与者，它们能激发儿童的创造性思维，但可能无法始终提供及时反馈。这些发现突显了同伴智能体在两种角色中的潜在设计改进与考量。
英文摘要：
In children's collaborative learning, effective peer conversations can significantly enhance the quality of children's collaborative interactions. The integration of Large Language Model (LLM) agents into this setting explores their novel role as peers, assessing impacts as team moderators and participants. We invited two groups of participants to engage in a collaborative learning workshop, where they discussed and proposed conceptual solutions to a design problem. The peer conversation transcripts were analyzed using thematic analysis. We discovered that peer agents, while managing discussions effectively as team moderators, sometimes have their instructions disregarded. As participants, they foster children's creative thinking but may not consistently provide timely feedback. These findings highlight potential design improvements and considerations for peer agents in both roles.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
